Cardiac arrhythmia is a deficiency in the heart electrical conducting system, which is manifested in an irregular heart-beat or abnormal heart rhythm. It may cause chest pain, loss of consciousness, blood clots, stroke, and in some cases death. In order to identify such arrhythmias, a physician must carefully probe the ECG signal for many characteristics. An important key for the success of arrhythmias identification and classification is identifying the AEA-waves, and examining the relation between them and the other signal elements. This task is difficult, particularly in arrhythmias in which the AEA wave is hidden in other waves or has different morphologies; frequently, an invasive procedure using intra-cardiac electrodes must be employed. It is therefore an object of the invention to provide a non-invasive method for identifying arrhythmias only be employing surface ECG.
Several approaches have been described for extracting atrial activity from ECG signals. One approach uses wavelet transformation, utilizing a special type of Fourier transformation to decompose the signals in different forms of representation in time and frequency that emphasizes the P wave (Ref. 3). But this method performs poorly on some arrhythmias, especially when the AEA wave falls inside the QRS complex, and it has not been tested on a sufficiently large group of patients. Another approach comprises QRST cancellation (Refs. 4, 5); the mean QRS beat is subtracted from the detected QRS locations in specific signal leads. This technique provides good specificity but poor sensitivity and/or accuracy. A different approach is includes uses the support vector machine (Ref. 6); after removing the detected QRS and T-waves, the slopes are calculated for each lead, and the AEA detection is performed assuming that the P waves have relatively high slope. This method showed satisfactory performances, but wasn't thoroughly validated on hidden P wave cases. Another approach uses the source separation (PCA/ICA, Refs. 7, 8), providing good performances for atrial fibrillation arrhythmia but worse performances in other cases, especially when the AEA-waves are linearly dependent on the QRS complexes. A relatively intuitive approach to the AEA detection relies on searching for local maxima (Ref. 9) or for a derivative zero crossing point (Ref. 10) in a specific search window respective to the QRS complex, but it does not have good performance in some arrhythmias. A new method for AEA-wave detection employs energy ratio between various time segments (Ref. 11). During the last few decades, many attempts have been made in order to create a robust and reliable arrhythmia classifier. A relatively uncomplicated approach suggests classification based on the frequency domain (Ref. 12). Using the median value of the signal's spectrum, ventricular arrhythmias and atrial fibrillation are differentiated from sinus rhythm. However, different supraventricular tachycardias (SVT's) are differentiated poorly. A different approach uses a Bayesian model (Ref. 13); it is trained to classify arrhythmias using features including QRS complex properties, ST-segment morphology and AEA-wave presence. It presents good results, but it only separates ventricular beats from non-ventricular beats; a well-known algorithm uses autoregressive modeling (Ref. 14). After a preliminary QRS detection it trains a model to create four autoregressive coefficients, for each arrhythmia type; each new test beat is then classified using generalized linear modeling. This method classifies ventricular arrhythmias, premature ventricular contraction (PVC), premature atrial contraction (PAC), sinus rhythm, and a general group of SVTs; however, the specific SVTs associated with the signal are not provided by this method. Another approach suggests a classification based on the RR-interval signal (Ref. 15); it first classifies each beat to a certain category and then classifies the tested signal to one of six arrhythmias, five of them ventricular. Many methods are solely intended to detect ventricular tachycardias, like ventricular fibrillation, which present an immediate life threatening situation (Ref. 16), but other clinically very important conditions are underrepresented among said methods. It can be concluded that many of the existing methods for arrhythmia classification are not designed for classifying SVTs or are inefficient in these cases. It is therefore an object of this invention to provide a method for detecting SVT by analyzing an ECG signal.
The fetal heart rate value and regularity are considered as parameters which can indicate fetal distress. Since fetal distress is a common indication for the necessity of Caesarean delivery, it is important to obtain a highly accurate fetal heart rate estimation, which on one hand assist the physician in early diagnosis of dangerous situations and on the other hand prevent false fetal distress detections, which might result in unnecessary operative actions. The Doppler ultrasound technique may provide a means for evaluating the fetal heart rate. However, it produces an averaged measure, and does not supply a convenient or accurate means for assessing the heart rate regularity and its fast changes. In contrary, the fetal ECG signal, may contain valuable information for characterizing the fetal heart rate, its variability and additional evaluation of the cardiac function. However, the existing means for obtaining the fetal ECG either provide a high amplitude maternal ECG (MECG) relatively to small amplitude fetal ECG (FECG), accompanied by additional bioelectric undesired noises, or said means comprise potential risks (fetal scalp electrodes, etc.). It is therefore a further object of the invention to detect fetal QRS (fQRS) from a surface ECG of the mother. It is another object of this invention to provide a method and a system for automatic detection of the atrial electrical activity and for classification of arrhythmias by analyzing ECG signals.
It is still another object of this invention to provide a method and a system for automatic detection of the fetal heart activity, particularly of fetal QRS complex, by analyzing abdomen ECG (AECG) signals of a pregnant women.
Other objects and advantages of present invention will appear as description proceeds.